计算机工程与科学2019，Vol.41Issue(2)：343-353,11.DOI:10.3969/j.issn.1007-130X.2019.02.022
基于注意力机制的文本情感倾向性研究
Text sentiment analysis based on attention mechanism
摘要
Abstract
As an important branch of sentiment analysis, short-text sentiment classification on social media has attracted more and more researchers' attention. To improve the accuracy of the short text target-based sentiment classification, we propose a network model that combines the part-of-speech attention mechanism with long short-term memory (PAT-LSTM). The text and the target are mapped to a vector within a certain threshold range. In addition, each word in the sentence is marked by the part-of-speech. The text vector, target vector and part-of-speech vector are then input into the model. The PAT-LSTM model can fully explore the relationship between target words and emotional words in a sentence, and it does not require syntactic analysis of sentences or external knowledge such as sentiment lexicon. The results of comparative experiments on the Eval2014 Task4 dataset show that the PAT-LSTM network model has higher accuracy in attention-based sentiment classification.关键词
注意力机制/长短时记忆网络/短文本/情感分析Key words
attention mechanism/LSTM/short text/sentiment analysis分类
信息技术与安全科学引用本文复制引用
裴颂文,王露露..基于注意力机制的文本情感倾向性研究[J].计算机工程与科学,2019,41(2):343-353,11.基金项目
上海市浦江人才计划(16PJ1407600) （16PJ1407600）
中国博士后科学基金(2017M610230) （2017M610230）
国家自然科学基金(61332009,61775139) （61332009,61775139）
计算机体系结构国家重点实验室开放题目(CARCH201807) （CARCH201807）
